Social Analytics System and Method for Analyzing Conversations in Social Media

ABSTRACT

Conversations in an online content universe are monitored. A social analysis module analyzes individual conversations between publishers in the online content universe. Publishers that influence a conversation are identified.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.12/558,788 filed Sep. 14, 2009, which is a continuation of U.S.application Ser. No. 11/680,537 filed Feb. 28, 2007, which applicationclaims priority to Provisional Application No. 60/777,975 filed on Feb.28, 2006, the contents of which are incorporated herein by reference.

FIELD OF THE INVENTION

The present invention is generally related to techniques to analyzeconversations within a conversational network. More particularly, thepresent invention is directed to analyzing the influence of social mediacontent and its publishers within a conversational network.

BACKGROUND OF THE INVENTION

The Internet is increasingly used as a platform for social media. Weblogs (blogs) and wilds are two common forms of social media. However,more generally social media may also include interactive aspects, suchas voting, comments, and trackback and take many different forms.Referring to FIG. 1A, social media generally describes onlinetechnologies and practices that people use to share opinions, insights,experience and perspectives with each other. Examples of social mediainclude social networks, blogging systems, media sharing platforms,online forums, and meme aggregators.

Social media is based on widely available tools that provide users theability to create links and trackbacks that tend to foster and describetheir trust relationships. There are several aspects of social mediathat foster trust relationships. One aspect of social media that fosterstrust relationships in social media is the level of dedication ofindividual publishers. Publishing social media content is an expressionof unique interest in a topic. Individuals participating in aconversation around this content invest time to read, trackback, tag,rate, and/or comment on what is being shared. The level of dedication ofthe publishers of social media and individuals participating inconversation around it is one factor that promotes trust within socialmedia. The trust relationships also develop due to the ability ofindividuals participating in a conversation to comment about postings toadd context and correct errors. Additionally, social media permits linksto be established between publishers. The links between publishersfoster the spread of ideas and also permits rapid feedback within thecommunity. Moreover, in social media influential and/or trustedpublishers and other participants in the conversation can lend theirweight to the veracity of the postings of other publishers, via links,comments, voting and the like. In the blogosphere, for example, aninfluential blogger can include links in a posting to other blogs, whichincreases the influence of the linked blog post on a discussion.

One aspect of social media is that it is highly conversational innature. As used in this patent application, an individual conversationin social media is a networked discussion about a specific topic betweensocial media publishers. A conversation can also include an interactionbetween at least one social media publisher and conventional onlinemedia, such as an online news source like CNN. A conversational networkis comprised of the individuals, sites, and pages participating inonline discussions about all topics. A conversation within the networkis about a specific topic. An individual publication corresponds to apost that is a single piece of media that can be located by a permalinkand which may also contain additional links. An individual publisher isa person or entity that posts social media (e.g., the person or entityassociated with one or more permalinked posts).

FIG. 1B illustrates a hypothetical example of how a conversation canflow within social media and also interact with conventional onlinemainstream media and corporate media. In the example of FIG. 1B, anillustrative example is that of a problem with a laptop battery. Insocial media the links between publishers within the social networkpermit different publishers to post Web content, provide comments, andpost links. As a result, a conversation about a topic can flow and beamplified through the social media and also interact with conventionalonline media. In the example of FIG. 1B, a publisher in a social network150 can vouch for the veracity of a posting of a blogger 152, increasingthe level of trust in the story posted by blogger 152. Blogger 152 caninclude a link to another site, such as a media sharing website 154having a video clip of the laptop battery problem and also to acorporate media website 153 having additional information about theproblem. An online forum 156 may have a favorable comment about thevideo clip and include a link to the media sharing website 156 alongwith another link to mainstream online media 158 posting the same clip.In this example, a Meme aggregator 166 may also have a link to onlinemainstream media 158. In the example of FIG. 1B, some of the aspects oftrust relationships can be observed such as publishers making commentssupporting the veracity of the postings of others, publishers makingcomments to correct errors, and publishers providing links to otherpublishers within social media and to conventional online mainstreammedia 158 and corporate media 153.

Conventional Internet search tools have proven inadequate forexamination of conversations within social media in terms ofunderstanding the interactions within a dynamic conversation.Conversations in social media can propagate and amplify with astonishingspeed. However, the information destination-oriented implementation ofconventional Internet search engines does not permit manycharacteristics of conversations in social media to be adequatelyunderstood.

A traditional Internet search engine has a crawling strategy forindexing a broad cross-section of the Internet likely to be of interestto general purpose users. Search engines typically generate results fora query that are described as relevant based on the search criteria anddistributed on a curve from “most relevant” to “least relevant,” whichcan be drawn on a relevancy curve, as in FIG. 1C. Thus as a hypotheticalexample, consider again the example of FIG. 1B. If a user inputs asearch query into a conventional search engine with query terms “AppleLaptop Exploding” they might receive 500,000 hits ranked by relevance. Aconventional search engine would present a relevant result by seekingpages on which the search term occurs most frequently and also take intoaccount some other relevance factors to rank the hits. Google's PageRank algorithm, for example, concatenates the number of sites pointingto each page with relevant search terms to identify the site mostpointed to by the greatest number of sites with high numbers of inboundlinks, using those pointers as a proxy for reliability of the data onthe page. If so many other sites point to the page, it must be the mostcorrect result for the search, the reasoning goes. This approach skewsresults to the top of the power curve in FIG. 1C giving sites thatproduce large numbers of articles and which are pointed to by othersites a disproportionate influence on the results, often long after thesite stops producing new relevant content. Thus, for example, referringagain to the hypothetical example of FIG. 1B, a conventional searchengine might give a disproportionate relevance to old articles aboutlaptop batteries.

Another problem of the conventional search engines is that they can begamed. Consider, for example, the Google search engine. Google isprimarily a ranking of web pages based on volumetric analysis. Google'sPage Rank calculates the rank of information on a page in response to asearch query by concatenating the number of explicit links from otherpages associated with the search topic to an undisclosed number ofdegrees (pages pointing to other pages through a Uniform ResourceIdentifier, or “URI”), the concept of authority in information has beenbuilt on the volumetric notion that the greater the number of linkspointing to a given page the more likely it is to be correct. Thisapproach can be gamed by launching sites that point to a page in orderto raise its authority (hence, Google must constantly adjust itsindexing algorithms to prevent gaming) and suffer from historicalskewing-sites. Volumetric determination of authority is prone to manyerrors and can be skewed by many factors that do not contribute to theuser's understanding of how the information reached its current form andauthority.

There are various modifications of conventional search engine technologythat have been proposed. For example, search engines have been developedwhich examine popularity of links by timeframe. Determining thepopularity by number of links pointing at a page within a giventimeframe, such as two week or a month from the current data, limitshistorical skewing. However, this improvement is still inadequate tounderstand a conversation in social media. The number of links withinthe given time frame may be general, including all links to a site, andtopic-specific, including just links that deal with a target searchphrase. As a consequence, sites which have general links will beover-weighted, and as a result will drown out topic-specificconversation.

Conventional search engines also have another limitation in that theytypically do not completely index social media. That is, the index in aconventional search engine does not capture sufficient information toproperly represent and/or analyze a conversation. Conventional searchengines are designed as general purpose engines to search the entire Weband have crawling policies that typically do not adequately index socialmedia. One limitation is that conventional search engines rely oncrawling of sites directly or capturing new information via ReallySimple Syndication (RSS) feeds to generate indices, which limits thereach of search in several important ways.

First, one limitation of conventional crawling is that recencyoverwhelms context. No Web index is complete, the best represent perhaps20 percent of the information on the Web, because the contents of pagesmust be captured by crawling sites from home page through the lastarchive page in order to be comprehensive. Because of limited resourcesand the more general focus of most search indices, crawls tend to coveronly a part of the total contents of many Web sites; a crawler, forexample, may only look at pages that are three pages below the home pageof a site. Since the most recent information tends to reside on archivalpages that may be more than three links deep on a site, a site'scoverage of a topic will be judged only on the content of the mostrecent postings rather than the entire body of work the site represents,which underweights sites that are deeply focused on a few narrow topics,such as “IT Management” or “Legal Practice” when other sites becomeinterested in those topics over a short period of time.

Second, another limitation of conventional crawling is that social mediaoften limits the comments exposed through RSS, which means thatconventional crawlers may not adequately index social media. Inparticular, few blogs expose their comments through RSS and those thatdo tend to separate the comments from the RSS feeds of main postings,eliminating or making far more difficult the analysis of comments inrelation to topics discussed on the site. This undercuts the indexer'sability to track cross-linking of discussions within comments andminimizes the role of communities that exist around particular siteswhen measuring the discussion of topics.

Third, another limitation of conventional crawling is that there is aping dependence. Indices that rely solely on RSS feeds depend onbloggers and publishers to “ping” the index server (that is, which sendan Extensible Markup Language Remote Procedure Call (XML-RPC) commandasking the index to review recent changes on the target site). Becausethere are many such indices and more appearing all the time, pinging hasactually fragmented the market and forced search companies to form acoalition to share pings, distributing updated posting information toall members. Ping-based systems that are not supplemented by directcrawls of sites do not successfully capture all activity on and aroundsites in networked conversations.

The various drawbacks of conventional search tools severely limits thecapability of individuals to analyze conversations in social media. Atone level, conventional search engines will often produce too many hits.For example, a conventional search engine, such as Google, may producemillions of hits from a simple query in which a few search terms areinput. On the other hand, a conventional search engine may fail toidentify many web postings, due to the previously described problemsassociated with RSS feeds and the fact that conventional search enginesindex only a fraction of the Web.

An even more serious weakness of conventional search engines is that aconventional search engine does not provide information directlyrelevant to understanding the dynamics of a conversation in socialmedia. In particular, the prior art search technology does not provide acapability to understand how conversations in social media areinfluenced and does not provide an understanding of potential trustedpoints of entry into a conversation.

Therefore, in light of the previously described problems, the apparatus,method, system, and computer readable medium of the present inventionwas developed.

SUMMARY OF THE INVENTION

A system analyzes social media, where the social media includes contentposted in an online content universe distributed on the Internet. Aconversation monitoring module monitors conversations in the socialmedia, where an individual conversation is a networked discussion ofpostings published on the Internet for a particular topic. A socialanalysis module analyzes content associated with a conversation forsocial relationships indicative of the evolution of the conversation. Inone embodiment the social analysis module includes an influence enginethat determines the influence of postings in a selected conversation. Inone embodiment, the influence engine determines the influence ofpostings and publishers who have multiple posts within a selectedconversation. In one implementation a trust filter is provided toperform trust filtering of the online content universe and aconversation index is generated of posts published in the social mediaaround trusted relationships.

One embodiment of a method includes generating a conversation index ofposts published in an online social media around trusted relationships.The conversation index is analyzed based on a user-defined topicdefinition of a selected conversation, where the selected conversationis a networked discussion between social media publishers about aparticular topic based on the user-defined topic definition. Theinfluence of posts in the selected conversation is determined. In oneembodiment, the influence of posts and publishers within the selectedconversation is determined.

BRIEF DESCRIPTION OF THE FIGURES

The invention is more fully appreciated in connection with the followingdetailed description taken in conjunction with the accompanyingdrawings, in which:

FIG. 1A illustrates social media types;

FIG. 1B illustrates the evolution of a conversation in social media;

FIG. 1C is an x-y relevance curve describing search results of aconventional search engine;

FIG. 2A illustrates a system for monitoring and analyzing conversationsin social media in accordance with one embodiment of the presentinvention;

FIG. 2B illustrates conversation processing in accordance with oneembodiment of the present invention;

FIG. 3A illustrates a process for determining influencers in aconversation in social media in accordance with one embodiment of thepresent invention;

FIG. 3B illustrates a process for determining the influence score of anindividual document based on attributes of the documents and neighboringdocuments in accordance with one embodiment of the present invention;

FIG. 4A illustrates an x-y curve displaying influence of a singlenetwork post or publisher at time 1 and time 2;

FIG. 4B illustrates two x-y curves displaying the influence of differentnetworked conversations at time 1 and time 2;

FIG. 4C illustrates two x-y curves displaying the multiplying effect ofcross-linking between two discussions displayed in FIG. 4B at time 3;

FIG. 5 is a diagram illustrating extraction of information form socialmedia in accordance with one embodiment of the present invention;

FIG. 6 is a network diagram illustrating the concepts of social degrees,strength of relationships and multi-variable social relationships inaccordance with one embodiment of the present invention;

FIG. 7 a illustrates interaction between a hosted service andconventional Search Engines in accordance with one embodiment of thepresent invention;

FIG. 7 b illustrates interactions between a hosted service and Web Sitesin accordance with one embodiment of the present invention;

FIG. 7 c illustrates interactions between a hosted service and BlogServer in accordance with one embodiment of the present invention;

FIG. 7 d illustrates interactions between a hosted service end-user andadvertising server applications in accordance with one embodiment of thepresent invention;

FIG. 8 is a network diagram illustrating XML protocols and theirinteraction with networked systems and services in accordance with oneembodiment of the present invention;

FIG. 9 is a flow chart of one embodiment of the data collectionprocesses in accordance with one embodiment of the present invention;

FIG. 10 illustrates a visualization of a networked conversation in oneembodiment of the system;

FIG. 11 illustrates an implementation of a public relations monitoringdashboard in one embodiment of the system;

FIG. 12 illustrates top-level of a dashboard and FIG. 13 illustrates anassociated detailed navigation guide of data social metrics in oneembodiment of the system; and

FIGS. 14-16 illustrate additional dashboard embodiments in accordancewith embodiments of the present invention.

DETAILED DESCRIPTION OF THE INVENTION I. Introduction And Overview

FIG. 2A is a block diagram illustrating a system in accordance with oneembodiment of the present invention. A conversation monitoring module210 monitors an online content universe 202 which include social media204 and which may also include conventional online content, such asmainstream online media 206 and corporate online media 208. In oneimplementation, conversation monitoring module utilizes a crawler (notshow in FIG. 2A) to monitor the online content universe 202, asdescribed below in more detail. One aspect of the conversationmonitoring module 210 is an identification module 220 to identify aconversation by, for example, providing sub-modules for locating trustrelationships 222, removing spam and splogs 226, and eliminating menusin content 224. A conversation processing module 230 includessub-modules for permalink identification 232, publication datadetermination 236, and content type determination 239. A conversationindex 240 is generated of postings within the social media, whereindividual postings have associated permalinks. Over the course of time,individual publishers, such as individual bloggers, may have manypostings indexed in the conversation index. A social analysis module 250includes an influence engine 252. The influence engine 252 includessub-modules to find influence signals 245 and identify influencers andtheir trusted networks 256. An individual influencer corresponds to anindividual posting, although it will be understood that a particularposting also has an associated publisher, publication site, etc. whichover time, may contain multiple influential postings within a certainconversation. An individual posting may also correspond to one or moreWeb pages.

Determining factors that influence a conversation is useful in manycontexts. The output of the influence engine may be used for differentapplications 260 such as a brand, product and reputation monitoringapplication module 261; online advertising targeting and deliveryapplication module 262; a publisher content network application module263 to enable a user to navigate between influential pages based on aset of influencers and publishers about a specific topic as determinedby the influence engine; a search engine optimization controlsapplication module 264; and influencer relationship management panelsapplication module 265.

FIG. 2B illustrates in more detail aspects of the conversationmonitoring module 210 related to conversation pre-processing. In oneembodiment, conversation processing module 230 implements contentclassification, permalink identification, and publication dateextraction. The gathered metadata is then stored in conversation index240. Data may come from trusted sources, such as long-time bloggers.However, note that for un-vetted information sources that an initialstage of spam/splog (spam blog) blocking is performed to filter out spamand splog. A trust filter in sub-module 222 verifies that the contentwhich is indexed is consistent with the content originating within oneor more networks of trusted relationships between publishers in theconversation index. In particular, the trust filtering may examine thecontent for one or more cues that indicate that the content isconsistent with a trust relationship network. In one embodiment, a trustfilter makes filtering decisions based on a criteria related to whetheror not the linking behavior is consistent with the type of linkingbehavior normally observed in trust relationships. For example, adecision not to filter an un-vetted post may be based on discovering apre-selected number of links from trusted posts to an un-vetted post. Inother words, it is desirable to filter out content which has one or moreindicia that indicates that it is not consistent with a trustrelationship in social media. For example, in the case of blogs,empirical studies of blogs may be performed to determine indicia that ablog is part of a trust relationship network and not a posting arisingfrom a malicious, deceptive, or untrustworthy source. As the Internetconstantly evolves over time it will be understood by one of ordinaryskill in the art that trust filtering requires empirical study to adapta trust filtering algorithm to changes in Internet usage over time todistinguish “normal” posting behavior in a trusted network from othertypes of postings which cannot be trusted. Note that in one embodimenthistorical data may be maintained on the credibility of individualpublishers over time. The combination of blocking spam/splog andperforming trust filtering improves the quality of the content that isindexed.

In one embodiment, a particular conversation is identified based on auser-defined input topic/target. As an illustrative example, in onembodiment a set of keywords, Boolean operators, and a URI (or set ofURIs) may be input by a user to define a topic of a conversation that auser wishes to explore. A search is then performed of the conversationindex, where the conversation index is a searchable index of webconversations that includes topical information, such as relevance; andrelationships between publishers, such as relationships betweencorporate sites, social and mainstream media. The conversation index 240accounts for implicit and explicit web user actions which drive theinfluence of social media posts and publishers. The influence engine 252calculates influence in social media networks based on various factors,such as relevance, occurrence, attention, popularity, and traffic. Theresults may, for example, be used to determine a ranking of influencersfor a specific conversation, a social map that is a visualrepresentation of relationships between posts and other participantswithin a conversation, a neighborhood of relationships around a socialmedia post, or other outputs.

FIG. 3A illustrates an example of a process that influence engine 252implements to analyze influence and determine a list of influencers fora specific conversation. In a first stage, the influence engine 252selects an initial candidate pool of documents for a conversation, witheach document having an associated publisher. In a practicalapplication, the conversation index may contain a large number ofdocuments that are relevant based only on keywords and Booleanoperators. The influence score is computed 316 using a selected set ofdimensions, using a weighting function to add additional dimensions inaddition to relevancy. In one embodiment at least seven dimensions areexamined, including page popularity 302, site popularity, 304, relevance306, recency 308, inlink recency 310, inlink page popularity 312, andinlink popularity 314. An inlink is an inbound link to a post in socialmedia. From the influence score, a list of influencers 318 for aspecific conversation is generated. Depending on the application, theoutput can include a list of publishers along with the documents havingthe maximal influence. As described elsewhere in this application, othermodifications include generating other types of information based on theinfluence scores, such as changes in influence over time. For example,there are many applications where it is useful to identify thosecontributions to online discussion, whether blog postings, articles inthe media or Web sites that are changing the nature of the discussionby: a) introducing new topics or interpretations of topics that mayalter impressions of a product/service/candidate; b) gaining or losingsupport in the discussion over time, which will ultimately reflect inchanged search results at some future date and, therefore, could alterperceptions of a product/service/candidate; and c) seeking communitiesof interest that, if combined, rapidly transform the influence of theirindividual perceptions and velocity with which those ideas travel acrossthe Net.

FIG. 3B illustrates additional aspects associated with computinginfluence of a document in accordance with one embodiment of the presentinvention. In one embodiment the influence of a document is calculatedbased on two different aspects. First, one aspect of the influence of aparticular document are properties directly associated with thedocument, such as relevance 332, recency 334, page popularity 336, andpage site popularity 338. Another aspect of the influence of thedocument are aspects of the document's neighbors, where a neighbor iseither documents that directly link to the document being considered forinfluence or links to a document through intermediary links to thedocument being considered (up to a pre-selected number of intermediarylinks, such as up to four links distant from the document). The finalinfluence score of a document is based on two scores, a first score thatweights different contributions of attributes of the document and asecond score that weights contributions of neighbors.

In one embodiment, neighbors are assigned to groups based on attributessuch as the relevancy of a neighbor (relevant/irrelevant),permalink/non-permalink, and dated/undated. Within a group of neighbors,contributions are summed to generate a group value. In one embodiment,the contribution of each neighbor to the influence of the document isbased on contributions of relevant permalink dated neighbors 341,contributions of relevant non-permalink dated neighbors 342,contributions of relevant permalink undated neighbors 343, contributionsof relevant non-permalink undated neighbors 344, contributions ofirrelevant, dated neighbors 345, and contributions of irrelevant,undated neighbors 346. The contribution of each neighbor to a group sumis a function of the neighbor's relevance, its own page popularity andpage-site popularity, and may also include the recency of the neighbor.A time decay function may be used to reduce the contribution of oldercontent of neighbors based on publication date. Neighbors in a group maybe grouped secondarily by a site identification with a “same site decay”function applied to their contributions to reduce the contribution oflarge quantities of links from the same site (large numbers of machinegenerated links do not reflect trust relationships and hence should begiven little weight). The same site decay function may also be appliedto document sorted descending by their recency and page popularity toensure that the most recent and popular neighbors from the same sitecontribute the most to the final influence score. A weighting functionis used to weight the contributions of the neighbors and from theaspects of the documents itself to determine a raw influence score forthe document. When all candidate documents have been assigned a rawinfluence score, a normalized influence score is computed for alldocuments.

Additional aspects, embodiments, and benefits of the present inventionwill now be described in more detail in the following sections. It willbe understood by those of ordinary skill in the art throughout thefollowing sections that the discussions refer to differentimplementations and applications of the previously described system asadditional examples for the purposes of illustration and description.

II. Dynamic Analysis Of Influence

One aspect of the present invention is that the influence of differentdocuments/publishers can be quantitatively measured and compared andanalyzed versus time. Influence can be measured for an individual postof a conversation. For example, an individual can make a one-time postand the influence of the post on a conversation measured. Additionally,aspects of the influence of a publisher who has made a number of postsabout a certain topic can also be measured.

One aspect of the present invention is that influence can be measuredover time. As illustrated in FIG. 4A, the difference in influence attime one (t₁) and time two (t₂) allows the present invention to trackthe changing number of connections around a particular idea, asexpressed in text on a Web page or, through language processing systemsthat may be connected to the system, such as audio and video sources.Over time, this allows the system of the present invention to single outsources whose influence is waxing or waning, allowing applications 260to choose when and where to engage the conversation. For example,objective criteria may be selected, such as a threshold level ofinfluence or a rate of change of influence. This permits a decision tome made when and where to engage the conversation. For example, aninfluential publisher and a time for engaging the influential publishermay be identified. Conversely, the objective criteria may be utilized tomake decision not to engage a conversation, such as if influence in aconversation begins to decrease.

The present invention can also be extended to support more advancedtechniques of influence analysis. In one embodiment multi-dimensionaltracking is supported. In this embodiment the system also views themarket in many dimensions rather than as one topical vector defined by asingle search parameter. This provides deep insight into how, when twocomplementary conversations intersect in a single blog posting, articleor other Web site, they can suddenly accelerate dramatically byachieving a geometrically larger audience through a mathematicallyexpanded discussion. In FIG. 4B, two different conversations are beingtracked. They are about different topics, “a+b” and “c,” until time two(02), when Conversation A adds the topic in Conversation B to its text.This happened in summer 2005, when blogger Jeff Jarvis, who blogs oftenon the fact that bloggers are not taken seriously when they criticizecompanies (in this case, the argument “c,” which is a frequent topic ofdiscussion among bloggers) linked the idea to his personal complaintsabout the lack of customer service support from Dell Computer (“a+b”).The conversational momentum increased at time 3 (“t₃” in FIG. 4C),leading to significantly more linking between sites discussing Dell,blogs and the media.

One embodiment of the present invention supports predictive analysis.The ability to identify emerging communities of discussion gives thepresent invention a unique capability to generate predictions of thevelocity and influence of ideas and individual contributors in a currentdiscussion using variables entered into what-if scenarios by anend-user. This embodiment provides if-then scenario-building featuresthat allow users to examine how social networks may be expected tobehave based on previous behavior and the potential impact of topiccrossover as illustrated in FIGS. 4A, 4B, and 4C.

In one embodiment, retrospective crawling is supported. FIG. 5illustrates in more detail an example of a process that may be used toassemble a chronological history of discussion and social relationships511 for conversation index 240. In this example, a “Persuadio MarketIntelligence” (PMI) system 505 crawls Web and ping data 507. The system505 is used to extract content, hyperlinks, and perform additionalanalysis, such as analyzing scripts, forms, and layout tags to identifydata created, the type of data, and social links 509. As illustrated inFIG. 5, the content of individual sites is examined to distinguish wheninformation appeared, what format it was produced in (e.g. blog posting,news article, comment about an article or blog posting), and construct anavigable history of social exchanges within the data. Relationshipsbetween people and organizations that created the data are reconstructedrevealing how ideas flowed between different sites, were amplified byindividual participants and what changes in perception were reflected indiscussions of the target topic.

One embodiment of the present invention permits conversations in socialmedia to be analyzed in ways not possible with conventional Internetsearch engines. Prior art search and blog monitoring tools focus onhistorical displays of the volume of discussion about a particular topicbased on conventional relevance scores, which is typically presentedonly as a histogram. Search matches based solely on conventionalrelevance matching does not expose which participants accelerated aconversation or what pages/postings increased the number of sites in aconversation about the topic through linking and social influence.Historical data, particularly about the previous interests ofparticipants and social relationships, provides the foundation forextrapolating future behavior as well as records of the role ofinfluencers in commercial brand perception.

Unlike traditional search engines, one embodiment of the presentinvention does not treat the whole Internet as a set of documents rankedon a single power curve. Instead, it dissects conversations based on atopic. Additionally, conversations may be dissected based on existingsocial relationships based on historical data, and the componentelements of documents and authors it is tracking to produce a morerefined power curve that includes relevant sites, which can be describedas an “attention lens.” For example, conversations may be going on about“road taxes in Lakewood,” which could refer to any number of cities indifferent states—none of the conversations is relevant to the others,but they are treated as a single subject by traditional search engines.By isolating the specific Lakewood through a calibration process thatproduces an initial attention lens, including analysis of the locationof participants sites, the language of the postings, the names of keyplayers in the conversation, and the expansion or contraction of linkrelationships over time can provide a very granular view of theinfluence within that discussion.

As sites and documents join or leave a conversation, they can befiltered by the linking of sites in the attention lens to reflect thechanging velocity and reach of the conversation. A conversationexpanding rapidly, either in terms of participants joining or thefrequency of postings on the target topic, has an increased probabilityof spilling over into other communities to become prominent subjects ofconversation. One embodiment of the present invention thus monitors nota single power curve summarizing the whole conversation about all topicstaking place on the Net, but instead identifies many small power curves,tracking the activity of each conversation discretely and cross-overbetween conversations over time to provide useful explanations of whyconversational patterns, influence and reach are changing.

Another embodiment of the present invention supports the capability toexamine discussions longitudinally, even retrospectively by extractingtime/date information from archival content, so that benchmarks ofinfluence may be established against which future conversational reach,velocity and influence may be measured. Through repeated crawls, thechanges are sought in the amount of influence individual postings andarticles have within discussions, providing extensive insight into whatindividual participants care most about, what they are likely to respondto and the probability that they may be drawn into discussion about aparticular topic.

Unlike other search engines and blog monitoring services, an embodimentof the present invention provides users the ability to reconstruct thehistory of a discussion from existing postings. In one embodiment thesystem's search features and Hyper Text Markup Language/ExtensibleMarkup Language (HTML/XML) parsing capabilities allow it to extract ahierarchy of information about each document a crawl finds, includingthe domain, site, page, posting body and time-created, as well asindividual comments that may appear on a page of text, whether a blog ora news story which includes a discussion thread. The system breaks downthe components of the page based on when information was added,providing a threaded view of conversations within a single site andacross multiple sites. Even where there are no explicit connectionsbetween sites, the system's ability to examine when ideas enteredconversations allows for analysis of un-attributed influence (e.g., aquoted passage that appears on a second site without a link to thesource site).

Additionally, unlike a conventional search engine, one embodiment of thepresent invention begins with a conversation index 240 optimized forsearching conversations in social media. As previously described, theconversation index of the present invention preferably utilizing trustfiltering to improve the quality content within the conversation index.Additionally, as described below in more detail, in one embodiment ofthe present invention additional variations on conventional crawlingtechniques are supported to index comments and other aspects ofconversations which are not typically indexed by conventional searchengines.

III. Trusted Network Analysis and Social Analysis Metrics

As previously described, the Social Analysis Module 250 utilizes toolsto analyze a conversation. These tools utilize various definitions basedaround an understanding of a social network having trustedrelationships, which will now be defined in more detail. It will beunderstood by one of ordinary skill in the art that for a particularimplementation, the definitions may vary from those described below,which are merely exemplary. However, what is important is therecognition that social analysis metrics may be developed based on anunderstanding of a social network which permits aspects of aconversation in social media to be objectively quantified and comparedto determine key influencers and other aspects of the conversation.

FIG. 6 illustrates a social network 600 having nodes 1, 2, 3, 4, 5, 6,and 7. The links between nodes are illustrated by arrows. As illustratedin FIG. 6, social network relationships have a directional sense, socialdegrees, strength between nodes, and multi-topic social relationships.Social networks are made up of links by one site to another. Anindividual story, may for example, propagate and be amplified (ordiminished) through a sequence of nodes based on the socialrelationships between the nodes. An individual node corresponds tosocial media posting at a site where social media is posted (i.e.,permalinked pages) and may have a variable number of links with othernodes. That is, the social media is posted on networked permalink pages.The links may be one-way or two-way. Additionally, an individual node,such as node 7, may have no links, and hence no social relationships.The strength of a relationships at a node will depend on the type oflink, in particular whether the link is a one-way or two-way links withother nodes; and the number of links (i.e., multiple links indicate astronger relationship than a single link). Additionally, topic relevanceis an aspect of the social network. An individual site may discussseveral different topics, as represented by the faces of the octagons,such that FIG. 6 represents a multi-topic relationship. In a multi-topicrelationship, opportunities exist to bridge communities with separateinterests and shared goals.

The social network illustrated in FIG. 6 is a useful starting point tounderstand different ways that the relationships can be characterized.Characterization of the relationships, in turn, may be useful toidentify indicators of a trust relationship and/or a trusted network. Aspreviously described, social media tends to foster trust relationshipsin which content is self-correcting. For example, blogs with an audienceare a priori relatively expert in the areas being linked to and they inturn link to other blogs in the same area whose authors are alsogenerally fanatical about stamping out misinformation. By the sametoken, good ideas presented in a blog tend to get amplified immediatelydue to the trust relationship. By carefully defining aspects of therelationships in a social network, such as that illustrated in FIG. 6,various attributes of trust relationships can be assigned definitionswhich permit influence and other aspects of the social network havingtrust relationships to be quantified and mapped.

An agent is a participant in a conversational exchange, which may be aperson, a document or file stored on the Internet, or a document oraudio/video record that can be analyzed to identify relationships andthematic influences. In FIG. 6, each node has an associated agent.

A degree is a unit of social measurement denoting a one-step connectionbetween two agents in a network. First-degree relationships include allsites with a direct connection to a site; second-degree relationshipsare two steps from the central or target site in a social networkanalysis. In FIG. 6, node 1 has a first degree relationship with nodes 3and 4. Node 1 has a second degree relationship with nodes 2, 5, and 6.

When used to describe computer-mediated social relationships a link is ahyperlink or other pointer embedded in the body of a Web site or pagethat can be followed, by clicking or activating the connection, bynetwork users from one file or page to another on the network. When usedto describe other social relationships, a link may be a spoken orwritten reference to another person or an idea, as expressed in text orin audio or video content.

Links have directionality. Some node relationships are one-wayrelationships in regards to how the nodes point to each other about aparticular topic. For example, nodes 3 and 4 provide inbound links(inlinks) to node 1 and receive no links from node 1. Other nodes havetwo-way relationships. For example, nodes 4 and 6 have a two-wayrelationship because they point to each other's content about aparticular topic.

There is a hierarchy of content. The system preferably delineatesbetween files, articles and pages on a Web site or network server,including an individual posting on a Weblog or online journal, treatingeach as an individual component of the conversation (“hierarchycomponent”) using a hierarchy of domains, sites, pages, posts, andcomments. A domain is the top-level domain name of a site or networkserver, such as “blogger.com” or “buzzlogic.com,” which may include manyindividual sites or blogs. A site is a unique network destination basedon a URI with a sub-domain of the domain name (e.g.“blogs.buzzlogic.com”) or a subdirectory that denotes an individual siteor blog (e.g., “blogger.com/mitchblog” or “cnn.com/andersoncooper”).Pages are an individual document that is part of a site or stored on anetwork server identified by a URI describing the full path to the file.Posts are individual components of a Weblog or other display interfacethat displays multiple entries based on user identity, time of day ordate. Comments are textual, audio or video responses attached to a pageor post by visitors to a site, such as reader responses on a newspaperWeb site or a Weblog.

Links are characterized as either outbound links or inbound links. Anoutbound hyperlink, network pointer or thematic reference in a recordingor on a site, page, post or comment. A inbound link is hyperlink,network pointer or thematic reference in a recording or on a site, page,post or comment that indicates a relationship with the target site,page, post or comment.

Agents are characterized as either active or inactive. An active Agentis an agent or site currently engaged in publishing about a specifiedsubject within a user-defined timeframe.

A social network analysis has a distribution of points in a map. Acenter can thus be defined as the target domain, site, page or post thatdefines the central point of a social network analysis or map.

The relevance of content can be defined by a focal exclusivity factor.Focal exclusivity is a value between zero (0) and one (1) that describesthe relevance of a site, page, post, or comment based on the totalnumber of matches to the search term(s) compared to other semanticallyimportant terms. It is calculated by extracting the search term(s) andother repeating terms in the target hierarchy component and dividing thenumber of occurrences of the search term(s) by the total number ofsemantically important repeating terms.

Relationships can be characterized by a social strength. The socialstrength is a value that describes the strength of the relationshipbetween two sites, people or ideas, based on the number of one-way andreciprocal links that connect them. The social strength of arelationship may be displayed on a scale as part of an index of allsocial relationships or used to calculate the median or average strengthof a social relationships maintained by the agent in order to assess therelative importance of individual relationships.

Relationships can be characterized by a social weight (influence). Asocial weight is a value that expresses the cumulative strength of allrelationships a domain, site, page, posting or comment based on acalculation with a user-defined weight for each variable:

(sum(social_weight_of_inbound_links)*user-defined weight[value=0>1]+count(inbound links)*user-defined weight[value=0>1)+count(outbound links)*user-defined weight [value=0>1])+focalexclusivity*user-defined weight [value=0>1].

Content can be characterized by the degree to which it associates eithera positive or negative characterization to the conversation. A tonefactor can be defined as a value between one (1) and negative one (−1)describing the ratio of positive and negative terms associated with thetarget search term(s). Tables are maintained of positive and negativewords for each workspace. Each positive word is counted as 1, eachnegative word is counted as −1. As a default, the sum of the values forthe positive and negative words is found by searching each hierarchycomponent for all positive and negative words. This sum is then dividedby the total words found to normalize the value to the range of +1 to−1. Proximity values, describing how closely search term(s) and toneterms co-occur, can be added to Tone.

A site can be ascribed a value indicative of the likelihood that thesite will engage in a discussion. A susceptibility factor is defined asa value between zero (0) and one (1) that describes the likelihood thata site will engage in a discussion about the target search term(s) thatis derived by the total number of occurrences of the search term(s)s andrelated terms divided by the total number of pages, posts or commentscreated during a user-specified timeframe.

The rate at which new agents join a calculation can be characterized. Avelocity factor is a value between zero (0) and one (1) that expressesthe frequency with which new agents are joining a conversation that iscalculated by counting the total number of pages, posts or comments thatmatch the search term(s), subtracting the previous crawl's total matchesto arrive at the number of new agents.

In one embodiment influence is characterized by a value that expressesthe conversational correlation between two or more agents about aspecified subject. Influence may be calculated using factors such asrelevance (how closely the text of a post by a publisher matches auser's query), occurrence (a count of the number of relevant postspublished over time by a publisher), attention (a score of relevance,and recency of inbound linking to an item in the conversation);popularity (total number of inbound links), and traffic (score thenumber of web users referred to y influencers, the number of page viewsthey accumulate, and/or other actions they take).

An influencer is defined as site, page or posting that has a socialweight greater than the median for a selected population of agents. Aninfluencer may or may not be related to target search terms, as somesites consistently lead the conversation by promoting conversation.

It is desirable to characterize how conversations are amplified. Anamplifier is defined as a site, page or posting that has a first-degreeoutbound social weight (all other variables unweighted or “0” [zero])greater than the median for a given target URI. An amplifier may or maynot be related to the target search terms, as some sites consistentlywiden conversations by repeating messages. A topic Amplifier is definedas a site, page or posting that is an Amplifier (see above) and containsthe target search terms and that repeats or points to the messages of aninfluencer.

Leadership can be defined. When describing the relationship between twoagents, the leader is a site, page or posting that receives more inboundlinks. When describing the position of a site, page or posting within aselected population of agents, a leader has a Social Strength greaterthan the median Social Strength of the whole network.

A volatility factor can be defined. Volatility is defined as a rangevalue (high=1; average=0.50; low=0) that describes the number of pagesor posts a site during the user-defined; may be a literal number basedon a user-defined scale or calculated by comparing the number of pagesor posts on the target site to a median value for the sample population.

A topic volatility is defined as a range value (high=1; average=0.50;low=0) that describes the number of pages or posts a site publishesabout the relevant search term(s) or related terms every 24 hours; maybe a literal number based on a user-defined scale or calculated bycomparing the number of pages or posts on the target site to a medianfigure for the sample population.

Background social relationships are characterized by the aggregatesocial weight of a domain, site, page or post without reference to thesearch term(s), which includes all link relationships.

A Meme correlation is a value between zero (0) and one (1) thatdescribes the correlation of specified search terms on two or more sitesover a user-specified time period.

The site reach may be defined by an integer value that describes thenumber of readers/viewers an agent addresses on a regular basis that canbe ascertained by analyzing visitor logs or through a proxy measurementor third-party auditor.

IV. Hosted Service Embodiments

Embodiments of the present invention may be implemented in differentways, such as within an enterprise or as a computer readable medium.However, one implementation of the present invention is as a hostedservice. Referring to FIGS. 7A, 7B, 7C, and 7D, one embodiment of thepresent invention is as a hosted service utilizing a server previouslydescribed in provisional application 60/777,975 as the “Persuadioserver” 702. The arrows and lines in FIGS. 7A, 7B, 7C, and 7D are usedto illustrate different modes of operation of the hosted service. In oneembodiment the hosted service is used to monitor, map, measure, andengage conversations. Full-text linking of relationships of social mediais preferably indexed to support generating a description and analysisof a networked conversations. As previously described, input criteria(e.g., keywords and URIs) may be input by a user to define a topic ofconversation. The service then monitors the conversation using thesocial analysis tools. The evolution of the conversation can be mappedand measurements generated of various metrics, such as influence or alist of influencers. Engagement in a conversation is preferablysupported, where an engagement is one or more posts and/or publisherswhere a user has entered the conversation. For example, engagement mayoccur via targeted advertisements or by identifying influentialindividual publishers for direct contact. The hosted service hasapplications such as managing crises, launching products, promotingbrands, public relations, marketing, competitive intelligence, andmonitoring problems associated with products. In one embodiment aPersuadio client application 704 includes a dashboard (described laterin this application in more detail) to guide users around conversations,influencers and content. The client application 704 may, for example,support setting up alerts to notify users when the volume ofconversation suddenly increases or other variances are exceeded or whena specific publisher (e.g., a specific blogger) joins a conversation.The dashboard may also generate a visual representation of aconversation network of social media, such as a social map ofrelationships between posts and other participants within aconversation.

In this example, the Persuadio server 702 implements the previouslydescribed conversation monitoring and social analysis. The Persuadioserver 702 has several different applications. One application is toprovide data to an ad server 706 for ad placement 707 to determine whenand where to place ads in blogs, web sites, or other social media.Another application is to provide data that may be passed on to aPersuadio client application 704.

FIG. 7A illustrates the relationship between the Persuadio server andconventional search engines. As illustrated in FIG. 7A, in oneembodiment, the Persuadio server can be implemented to query third-partysearch engines to assemble and analyze results for social relationships.The search results may be used to provide results annotated with socialdata to the Persuadio client application 704 or to configure additionalweb crawling and data gathering for social analysis.

FIG. 7B illustrates the relationship between the Persuadio server 702and web sites 712. As illustrated in FIG. 7B, the Persuadio server 702preferably uses web crawling tools 714 to collect the complete HTML 713from each page of a web site. The HTML is analyzed to identifycomponents of the Web page, collect and store relevant text and data,such as HTML tags that indicates the role of information in adiscussion. Web site social influence data can be forwarded to adplacement servers, combined with blog and other data to create a view ofthe entire network of discussion, and delivered into the Persuadioclient application 704.

FIG. 7C illustrates the relationship between the Persuadio server 702and blog sites 722. As illustrated in FIG. 7C, the Persuadio server alsopreferably has a capability to capture data from social media, such asblogs. In one embodiment the Persuadio server 702 captures data fromblogs using web crawling 714 and XML-RPC pings 734 generated by blogs orcollected at a centralized pint server 736, such as pingomatic.com orVerisigns's Weblogs.com. When crawling a blog, the full HTML ispreferably captured from the page, using tags to identify components ofthe page, differentiating between individual postings, comments andtrackbacks displayed on the page. Each part of the page is important tounderstanding a specific part of a networked discussion. Pings may alsobe used to initiate a crawl of a page.

FIG. 7D illustrates the relationship between the Persuadio server 702and the generation of outputs. As illustrated in FIG. 7D, in oneimplementation, the Persuadio server 702 generates an XML feed 742 thatmay be used by other applications or servers. The XML feed 742, may forexample, provide information to improve ad targeting, such asidentifying key publishers and key times to insert an advertisementrelated to a particular topic. The XML feed 742 may also, for example,identify a list of key influencers of a conversation, providevisualization of networked discussions, or other outputs. Additionally,the XML feed 742 may be used to create visualization, spreadsheets, orother information for an end-user to understand a networked discussion.For example, an end-user may want a visualization or list identifyingkey influencers in a conversation, thresholds for the evolution of aconversation (i.e., key times in the development in a conversation), ora map illustrating the growth of a conversation.

The hosted service is preferably implemented as a scalable system andmethod for collecting data, calculating social metrics and expressingthose metrics to describe conversational networks where individuals andentities exchange Web links, attention and other information aboutspecific topics. The hosted system may be implemented as a collection ofsoftware functions and the configuration of those functions for optimaldata gathering, analytics processing and publishing of resulting metricsas a stable standard protocol. The hosted service examines the sourcecode of Web pages and documents stored on the Internet which may containcontributions by many people and links representing additionalparticipants' ideas to identify individual components of socialinteraction, such as an article, Weblog posting, or reader comment eachof these components has social characteristics, including influencewithin the conversation as a whole, influence on specific contributors,tone (positive or negative) and probabilities that it will continue toparticipate and the degree of that participation. Additionally, in theaforementioned embodiment, each agent or component of the conversationwill have social characteristics that are dependent upon the specifictopic under discussion, which can be correlated to its relationship tothe participants' overall influence in a selected timeframe.

FIG. 8 illustrate interactions in a networked environment accessingcontent on the Internet 820 using an embodiment having a search server830, longitudinal database 840, client or third party application 850.The system preferably supports importing of data describing discussionsbetween people conducted in person, through email, short messagingsystems or in other recorded exchanges It includes a metadata format forexpressing those statistical metrics for use in a variety ofapplications, including but not limited to media monitoring, advertisingpricing and placement in a document, presenting search results,targeting marketing communications and network visualization asillustrated in FIG. 8. The metadata XML Protocol, which in oneembodiment uses XML Namespaces, expresses multiple variables that can beused in calculations of influence value and/or positional coordinatesdescribing a social relationship. XML Namespaces, provides an extensiblefoundation for communicating social metrics for use by a variety ofend-user applications. The metadata protocol supports variable-sizedtextual and integer formats in all international character sets toprovide many dimensions of social data.

The XML Protocol is a standardized format for storing social datagenerated by the system, which may be used for output tocompany-proprietary or third-party applications configured to interpretthe data or for input of data from a company-proprietary or third-partydata source. Specific fields may be used for attributes related toanalyzing a conversation.

The table below illustrates some exemplary field definitions of the XMLProtocol. Applications of the XML Protocol will be described later inmore detail.

-   Attributes Meaning-   TargetURI The URI of the target posting or page described (multiple    items may exist on the same page).-   SiteURI The top-level URI of the site where the target posting or    URI is located.-   Topic Key search terms—Describes the topic of the discussion.-   Relevance Relevance of the target URI based on Topic [Ranked 0 to 1,    on a scale]-   Center A mapping-specific field that defines the center of the    network. If the search is a general query about sites around a    specific site or URI, this URI defines the center of the network.    This URI will not match the TargetURI unless it is the target URI.-   TargetWeightURI Social weight of the target URI within current    network (the network is defined by the key search term).-   Attributes Meaning-   TargetWeightSite Social weight of the top-level URI where the target    posting or URI is located within current network (the network is    defined by the key search term).-   InboundsURI List of URIs pointing to the target URI, with    time-created.-   OutboundsURI List of URIs pointed at by the target URI with    time-created.-   InboundsSite List of URIs pointing to the site where the target URI    is located.-   OutboundsSite List of URIs pointed at by the site where the target    URI is located.-   StrongLinksURI List of sites strongly connected to the target URI as    a [user configured] percentage of total connections. (One site may    account for 10 percent, or 100 might)-   StrongLinksSite List of sites strongly connected to the top-level    URI where the target URI is located as a [user configured]    percentage of total connections. (One site may account for 10    percent, or 100 might)-   FocalExURI Focal exclusivity of target URI (percentage of the target    posting or page discussing the search topic—based on generic and    custom lexicons).-   FocalExSite Focal exclusivity of the top-level URI (percentage of    the site where the target URI is located that is about the search    topic—based on generic and custom lexicons).-   Tone Positive-Negative tonality based on generic or custom thesauri    (Ranked+1 to −1)

V. Illustrative Calibration, Crawl, and Social Analysis Methodology

As previously described, one aspect of the present invention is theconversation monitoring module may use a crawler to populate theconversation index. Additionally, as previously described the influenceengine may use information on how document are linked to neighbors(directly or indirectly through intermediate links) to determine aninfluence score. It is therefore desirable to perform calibrations andoptimizations of the crawling and social analysis.

An exemplary calibration methodology, crawl methodology, and socialanalysis methodology will now be described in more detail with referenceto FIG. 9. Some of the aspects of a practical system includecalibration, crawl methodology, and social analyzers.

A calibration process includes an initial series of crawls to develop afocused index of representative influential sites that define aconversational market. The calibration process utilizes a Web crawler,or “spider” application 905 and search engine-based analyzers workingfrom a database-driven collection of query phrases. A database providesthe storage volume for results of the current and historic crawls. AnHTML/XML parser 915 implements a process that uses hints stored in adatabase to extract the hierarchy and chronology information from theraw data in the crawl database. A LINKLOGGER 920 implements a processthat extracts and records all Source URI->Target URI relationships,recording them in crawl database.

The system can configure a research crawl based on a variety ofuser-selected inputs to define an initial target search. One example ofa user-selected input is to define an initial target search based onsingle URI using the “link:” search command to capture all sites linkingto the site-level URI of the target. Alternately, a user may provide anetwork of target URI's to define an initial target search. The initialtarget search is further limited by searching for target terms. As anillustrative example, a search for pages matching target terms mayinclude 32 different search indices with public or private applicationprogramming interfaces. The system may be configured to begin itscrawling based on a defined number of results after eliminatingredundant URIs and normalizing the ranking scores used in differentindices to a single scoring system. In the exemplar described herein,the system selects 1,000 results.

Exemplary Calibration Process

An exemplary calibration process includes five calibration steps.

In a first calibration step (Step 1), using the initial target data set,the system begins by placing a collection of seed Uniform ResourceLocators (URLs) on a queue, prioritized by the relevance of the page. Aseparate process pulls the most relevant URL from the queue and crawlsall of its outlinks, continuing to place URLs on the queue until itcrawls two degrees away from relevant URLs. In addition, the system usesbackward link references to discover all links pointing to a page on thequeue and retrieves those URLs, adding them to the queue by priority ofthe child page.

In a second step of calibration (Step 2), the system analyzes thecontent and code of documents captured in the 1^(st) degree crawl [seeabove] using the HTML/XML Parser 915. It breaks down the content intocomponent parts based on a hierarchy (domain, site, page, posting,comment) using code parsing hints stored in the database. Additionally,the system extracts time stamp information to establish the chronologyof the information, tracking the date and time when components of thehierarchy were created (a page may have postings or comments createdafter the page creation date, for example). The components of thehierarchy with social characteristics to be tracked by the system aresites, pages, posting and comments.

The system also extracts all outbound links and creates an index of thecreators (page creator, author, poster or commenter) identities, whichare associated with source URIs (e.g., the URI of the commenter's blog),which can be crawled in the next step of the analysis.

Based on the user-specified timeframe of the calibration, the system mayor may not collect content created and posted to the Internet on orbefore a user-defined date. If it does collect historical data, this isstored in the crawl database.

Data stored in fields based on THE XML PROTOCOL, all URI types may belisted in a single entry, the lowest in the hierarchy being the targetURI described by other fields in the database for this entry/row. Eachlayer of the hierarchy inherits from the lower layers, e.g. DOMAINinherits the SITE characteristics: DOMAIN_URI: The top-level domainname, e.g. buzzlogic.com; SITE_URI: The URI, including sub-domains ordirectories that indicate individual sections of a site controlled by asingle author/editor or group of authors/editors, e.g.www.bloghost.com/Tomblog or “blogs.bloghost.com” or “money.cnn.com”;PAGE-POST_URI: The absolute URI of a single document stored on a site orserver that includes a search term or other statement by an author thatthe user desires to monitor; COMMENT_URI: The absolute URI of a singlecomment, trackback or other reader-annotation to a page or post.

LINKLOGGER 920 examines each component of the hierarchy identified byHTML/XML Parser to find all outbound links, which are recorded in crawldatabase (e.g. “source URI”->“target URI” until all links are recorded).Data stored in fields based on THE XML PROTOCOL: *OutboundsURI:URI->URI: [time created]

Step 2 is repeated for the 2^(nd) degree and source URN of participants,collecting all data and code to extract all outbound links, chronology,participant identity source URIs. Step 2 may then be repeated for 3^(rd)and further degrees as specified by the user.

In a third step of calibration (Step 3) a check is performed on the dataset of URIs/documents created in Step 2 for inbound, outbound andbi-directional link relationships within the network and, in the lastdegree, outbound links to non-network sites.

A calculation is performed of the social strength for each pair of sitesbased on the directionality of the links as indicated by the directionalarrows below as follows:

1) Calculate site A->B number of links;2) Calculate site B->A number of links;3) Calculate site A<->B links within individual articles, postings,comments*1.5 (multiplier for bidirectional relationships);4) Determine A->B link relationship strength across whole network;5) Determine median A->B link relationship strength across wholenetwork;6) Score “1” for strong relationships (Top 30 percent);7) Score “2” for normal relationships (Middle 30 percent); and8) Score “3” for weak relationships (Bottom 40 percent)

Data is stored in fields based on following THE XML PROTOCOL:

1) StrongLinks[HIERARCHY Level]: List of sites with first-degree socialweights, without Focal Exclusivity weights, in the top 30 percent; and

2) StrongLinksNoFoc[HIERARCHY Level]: List of sites with first-degreesocial weight with Focal Exclusivity weighted strongly in the top 30percent.

In a fourth step of calibration, a calculation is performed to calculatesocial weight of each level of the hierarchy, excluding focalexclusivity,

as we are concerned about link relationships at this point: (total #inbound links*weight [value 1->0])+(total # outbound links*weight[value+1->0])+(focal exclusivity 0)

This calculation produces social weight for: Domains; Sites; Pages,Postings, and Comments.

Next, a calculation is performed of the social weight including focalexclusivity for each level of the hierarchy: (total # inboundlinks*weight [value 1->0])+(total # outbound links weight[value+1->0])+(focal exclusivity*weight [value 1]). This data indicativeof strong links is stored in fields based on THE XML PROTOCOL:

StrongLinks[HIERARCHY Level]: List of sites with first-degree socialweights, without Focal Exclusivity weights, in the top 30 percent

StrongLinksNoFoc[HIERARCHY Level]: List of sites with first-degreesocial weight with Focal Exclusivity weighted strongly in the top 30percent

Focal exclusivity data is stored in fields based on THE XML PROTOCOL:*FocalEx[HIERARCHY Level]: Value.

Each level of hierarchy component above the identified components in thehierarchy is updated to reflect new focal exclusivity scores based onall lower hierarchy components. This data is stored in fields based onTHE XML PROTOCOL: *FocalEx[HIERARCHY Level]: Value.

Each URI/hierarchy component is ranked for social weight w/o focalweight. This data is stored in fields based on THE XML PROTOCOL:*TargetWeightNoFoc[HIERARCHY Level]: Value.

Each URI/hierarchy component is ranked for social weight w/ focalweight. This data stored in fields based on THE XML PROTOCOL:*TargetWeight[HIERARCHY Level]: Value.

Each level of a hierarchy component above identified components in thehierarchy is updated to reflect new social weight w/o focal weightscores (background social connectedness without regard to topic). Thisdata is stored in fields based on THE XML PROTOCOL:*TargetWeightNoFoc[HIERARCHY Level]: Value

Each level of hierarchy component above identified components in thehierarchy is updated to reflect new social weight w/ scores (backgroundsocial connectedness based on the search terms). This data stored infields based on THE XML PROTOCOL: *TargetWeight[HIERARCHY Level]: Value.

The results of each day's calibration process are stored in the crawldatabase for use in the next day's crawl. If the system has beenconfigured to capture historical data for use in analysis orbenchmarking, that data is stored in crawl database, according to theparameters described in the crawl section below.

In a fifth step of calibration (step 5), after daily crawls, newdomains/sites/postings/comments are added and all analysis in Steps 1through 4 is conducted during the calibration period. Additionalcalculations are performed to aggregate median social weight of allsites that include the search terms. A selection is made of all OR 395sites above the median social weight of the network, descending from thehighest score. A selection is then made of all sites that include searchterms with Strong pair-wise social strength relationships. A calculationis then made of the median social weight of the resulting index ofsites. The result is the permanent index that will be crawled each day,adding new sites daily, conducting a “recalibration every day to addnewly discovered Uniform Resource Locators (URLs) to the network,maintaining a complete record of all sites for periodic re-crawling. Theresults of the calibration process are stored in the crawl database foruse by the crawler.

Exemplary Crawling Methodology

The crawling system implements steps the system takes on a user-definedschedule to extract current social metrics for a conversationalenvironment. The crawling system includes a crawler and searchengine-based analyzers working from a QUERIES table 925. The crawldatabase is the storage volume for results of the current crawl. A(HTML/XML) Parser 915 is a process that uses hints stored in the crawldatabase to extract the hierarchy and chronology information from theraw data in the crawl-database. A LINKLOGGER 920 is a process thatextracts and records all Source URI->Target URI relationships, recordingthem in CURB DB 910.

In a first step of crawling, at crawl time, the crawler examines thedatabase table of queries for search parameters. On the first day of thecrawl, it uses the calibrated query table generated by the system duringthe calibration process. Each successive day, it uses the seed URIscontained in the calibrated query table PLUS all URIs identified asrelevant by the system and stored in the StrongLinks andStrongLinksNoFoc fields of the crawl database.

The crawler captures page content and code for sites listed in thePermanent Index created during Calibration, plus first-, second- andn-degree links for all content added since the previous crawl, storingall content and code in crawl database.

In a second step of crawling, the search engine builds an index based onoccurrence of the search terms according to user-specified parameters(e.g., proximity, tone, etc.)

The search engine records all new occurrences of search terms in thecrawl database

HTML/XML Parser 915 examines the content of the new data from crawledpages, using hints stored in the crawl database, to extract parts ofpages that fall into different components of the Hierarchy.

Data stored in fields based on THE XML PROTOCOL, all URI types may belisted in a single entry, the lowest in the hierarchy being the targetURI described by other fields in the database for this entry/row. Eachlayer of the hierarchy inherits from the lower layers, e.g. DOMAINinherits SITE characteristics:

1) DOMAIN URI: The top-level domain name, e.g. buzzlogic.com;

2) SITE_URI: The URI, including sub-domains or directories that indicateindividual sections of a site controlled by a single author/editor orgroup of authors/editors, e.g. www.bloghost.com/Tomblog or“blogs.bloghost.com” or “money.cnn.com”;

3) PAGE-POST_URI: The absolute URI of a single document stored on a siteor server that includes a search term or other statement by an authorthat the user desires to monitor; and

4) COMMENT_URI: The absolute URI of a single comment, trackback or otherreader-annotation to a page or post.

The LINKLOGGER 920 examines each component of the Hierarchy identifiedby the HTML/XML Parser 915 to find all outbound links, which arerecorded in the crawl database (e.g. “source URI”->“target URI” untilall links are recorded). Data is stored in fields based on THE XMLPROTOCOL: *OutboundsURI: URI->URI: [time created]

In a third step of crawling, each URI/hierarchy component ranked forfocal exclusivity. Data is stored in fields based on THE XML PROTOCOL:*FocalEx[HIERARCHY Level]: Value.

Each level of hierarchy component above the identified components in thehierarchy is updated to reflect new focal exclusivity scores based onall lower hierarchy components. The data is stored in fields based onTHE XML PROTOCOL: *FocalEx[HIERARCHY Level]: Value.

Each URI/hierarchy component is ranked for social weight w/o focalweight. The data is stored in fields based on THE XML PROTOCOL:*TargetWeightNoFoc[HIERARCHY Level]: Value.

Each URI/hierarchy component ranked for social weight w/ focal weight.The data is stored in fields based on THE XML PROTOCOL:*TargetWeight[HIERARCHY Level]: Value.

Each level of hierarchy component above identified components in thehierarchy is updated to reflect new social weight w/o focal weightscores (background social connectedness without regard to topic). Datais stored in fields based on THE XML PROTOCOL:*TargetWeightNoFoc[HIERARCHY Level]: Value.

Each level of hierarchy component above identified components in thehierarchy is updated to reflect new social weight w/ scores (backgroundsocial connectedness based on the search terms). Data is stored infields based on THE XML PROTOCOL: TargetWeight[HIERARCHY Level]: Value.

In a fourth step of crawling, each current URI/hierarchy componentanalyzed for pairwise linking to identify strong first-degree socialrelationships (background strong relationships).

Each current URI/hierarchy component analyzed for pairwise linking totargets with search term matches to identify first-degree topic-relevantstrong relationships. Each current URI compared to the crawl databasefor previously known link relationships at each hierarchy level, and theresults extracted and stored in the crawl database. Data ISstored infields based on THE XML PROTOCOL:

StrongLinks[HIERARCHY Level]: List of sites with first-degree socialweights, without Focal Exclusivity weights, in the top 30 percent;

StrongLinksNoFoc[HIERARCHY Level]: List of sites with first-degreesocial weight with Focal Exclusivity weighted strongly in the top 30percent.

Exemplary Social Analysis Methodology Step 1: Network Weaving

At the conclusion of the crawl sequence, the social analysis moduleperforms a series of database searches on the crawl database to fleshout link relationships by topic/keyword and between all sites in thesocial network population. URIs stored in the database and arecross-referenced to their historical content (all pages with relevantcontent are stored in the database; the content of irrelevant pages aredumped but the URIs and times created are stored for potential futureretrieval to do further analysis).

All inbound links to a given TARGET_URI are identified and stored infields based on THE XML PROTOCOL: InboundsURI: List each URI and timecreated.

All outbound links from a given TARGET_URI are identified and stored infields based on THE XML PROTOCOL: OutboundsURI: List each URI and timecreated.

Proceeding up the HIERARCHY, all inbound and outbound links for eachidentified HIERARCHY component are captured and stored in fields basedon THE XML PROTOCOL:

InBounds[Hierarchy Level]: List each URI and time created

Outbounds[Hierarch Level]: List each URI and time created.

Step 2: Amplifier Mapping

The content of CURR_DB 910 and HISTORICAL_DB 950 are queried forHIERARCHY components matching the search terms and the times those URIswere created. The results are parsed to produce a chronology of theappearance of related content on the network and the flow of backgroundrelationships. The chronology is examined for pages that areinbound-linked to by more than the median number of pages linked to inthe sample population.

All Amplifiers for a given TARGET_URI are identified and stored infields based on THE XML PROTOCOL: Amplifiers: List each URI and timecreated. URIs ranked by highest number of links to the target URIdescending.

Proceeding up the HIERARCHY, all Amplifiers for each identifiedHIERARCHY component are captured and stored in fields based on THE XMLPROTOCOL: Amplifiers[Hierarch Level]: List each URI and time created.Ranked by highest number of links to the target URI descending.

At the Site level (that is, the blog or site controlled by a singleauthor/editor or group of authors/editors), Amplifiers are analyzed forall site relationships and the topic-based relationships the site hasover time and stored in fields based on THE XML PROTOCOL:

SiteAmplifier: List each site, the number of inbound connections fromthe site to the target site, and the times links created. Ranked byhighest number of links to the target URI descending;

TopicAmplifier: For each search term the site contains, list the sitesthat have linked to pages containing those terms and the times linkswere created. Ranked by highest number of links to the target URIdescending.

Finally, the individual Amplifier chronologies are examined to identifysites that have linked to the target site—both generally and to pagescontaining search terms—within a user-defined timeframe and stored infields based on THE XML PROTOCOL:

RecentAmplifiers: List each site, the number of inbound connections fromthe site to the target site, and the times links created. Ranked byhighest number of links to the target URI descending during thespecified timeframe.

RecentTopicAmplifiers: List each site, the number of inbound connectionsfrom the site to the target site, and the times links created. Ranked byhighest number of links to the target URI descending during thespecified timeframe.

Step 3: Leader/Follower Analysis Step A

In this step, we are looking for the strong relationships within smallportions of the social network and calculating the likelihood that thoserelationships will produce reliable leader-follower behavior. A site maybe both a leader and a follower.

Using the Amplifier chronologies created in Step 2, calculate the normaldistribution of inbound link relationships between all source and targetURI for the sample population over the user-defined timeframe. We'relooking for the distribution of URIs created:URIs point to each URIcreated.

Find the median and variance within the distribution of linkrelationships. Then calculate the probability that any URI created willreceive an inbound link. Store the probability for the entire sample foruse in other calculations.

Next, break down the normal distribution by percentage, taking each10-percent bracket and calculating the probability a URI created in thattenth of the distribution will receive an inbound link. Store theprobability for each bracket for use in other calculations.

To find site-level leader relationships, eliminate all non-repeatingSite relationships from the URI list, so that the sample contains onlyURIs in sites that garner repeat inbound links from other sites.

Calculate the normal distribution of inbound link relationships betweenall source and target URI for the Site relationships sample populationover the user-defined timeframe.

Find the median and variance within the distribution of Site linkrelationships. Then calculate the probability that any URI createdwithin one of these sites will receive an inbound link. Store theprobability for the entire sample of Site relationships for use in othercalculations.

Next, break down the normal distribution by percentage, taking each10-percent bracket and calculating the probability a URI created in thattenth of the distribution will receive an inbound link. Store theprobability for each bracket for use in other calculations.

Step B

In this step, we are looking for the strong link relationships based onthe keyword focal exclusivity.

Using the TopicAmplifier chronologies created in Step 2, calculate thenormal distribution of inbound link relationships between all source andtarget URI for the sample population over the user-defined timeframe.We're looking for the distribution of URN created:URIs point to each URIcreated.

Find the median and variance within the distribution of linkrelationships. Then calculate the probability that any topic-specificURI created will receive an inbound link. Store the probability for theentire sample for use in other calculations.

Next, break down the normal distribution by percentage, taking each10-percent bracket and calculating the probability a URI created in thattenth of the distribution will receive an inbound link. Store theprobability for each bracket for use in other calculations.

To find site-level topic-specific leader relationships, eliminate allnon-repeating Site relationships from the URI list, so that the samplecontains only URIs in sites that garner repeat inbound links from othersites.

Calculate the normal distribution of inbound link relationships betweenall source and target URI for the Site relationships sample populationover the user-defined timeframe.

Find the median and variance within the distribution of Site linkrelationships. Then calculate the probability that any URI createdwithin one of these sites will receive an inbound link. Store theprobability for the entire sample of Site relationships for use in othercalculations.

Next, break down the normal distribution by percentage, taking each10-percent bracket and calculating the probability a URI created in thattenth of the distribution will receive an inbound link. Store theprobability for each bracket for use in other calculations.

Step C

Assign URI- and Site-level probabilities to each URI in the database.These probabilities are a range that can be applied to estimating thelikelihood any site, blog, posting or comment will instigate morediscussion.

Assign topic-specific URI- and Site-level probabilities to eachtopic-specific URI in the database. These probabilities are a range thatcan be applied to estimating the likelihood any topic-specific site,blog, posting or comment will instigate more discussion.

Step D

“Leaders” are identified from the sample population. They are sites withthe highest average URI-level probability to attract multiple links

“Topic Leaders” are identified in the sample population. They are siteswith the highest average topic-specific URI probability to attractmultiple links.

Step E

Follower analysis identifies sites most likely to be drawn into aconversation, described as “Susceptibility.”

Using the Amplifier chronologies created in Step 2, calculate the normaldistribution of outbound link relationships of all URIs in the samplepopulation over the user-defined timeframe.

Find the median and variance within the distribution of linkrelationships. Then calculate the probability that any URI created willinclude an outbound link. Store the probability for the entire samplefor use in other calculations.

Next, break down the normal distribution by percentage, taking each10-percent bracket and calculating the probability a URI created in thattenth of the distribution will include an outbound link. Store theprobability for each bracket for use in other calculations.

To find site-level follower relationships, eliminate all non-repeatingSite relationships from the URI list, so that the sample contains onlyURIs in sites that include repeat outbound links to other sites.

Calculate the normal distribution of outbound link relationships betweenall source and target URI for the Site relationships sample populationover the user-defined timeframe.

Find the median and variance within the distribution of Site linkrelationships. Then calculate the probability that any URI createdwithin one of these sites will include an outbound link. Store theprobability for the entire sample of Site relationships for use in othercalculations.

Next, break down the normal distribution by percentage, taking each10-percent bracket and calculating the probability a URI created in thattenth of the distribution will include an outbound link. Store theprobability for each bracket for use in other calculations.

Step F

In this step, we are looking for the susceptibility based on the keywordfocal exclusivity.

Using the TopicAmplifier chronologies created in Step 2, calculate thenormal distribution of outbound link relationships between all sourceand target URI for the sample population over the user-definedtimeframe.

Find the median and variance within the distribution of linkrelationships. Then calculate the probability that any topic-specificURI created will include an outbound link. Store the probability for theentire sample for use in other calculations.

Next, break down the normal distribution by percentage, taking each10-percent bracket and calculating the probability a URI created in thattenth of the distribution will include an outbound link. Store theprobability for each bracket for use in other calculations.

To find site-level topic-specific follower relationships, eliminate allnon-repeating Site relationships from the URI list, so that the samplecontains only URIs in sites that create repeat outbound links to othersites.

Calculate the normal distribution of outbound link relationships betweenall source and target URI for the Site relationships sample populationover the user-defined timeframe.

Find the median and variance within the distribution of Site linkrelationships. Then calculate the probability that any URI createdwithin one of these sites will include an outbound link. Store theprobability for the entire sample of Site relationships for use in othercalculations.

Next, break down the normal distribution by percentage, taking each10-percent bracket and calculating the probability a URI created in thattenth of the distribution will include an outbound link. Store theprobability for each bracket for use in other calculations.

Step G

Assign URI- and Site-level outbound linking probabilities to each URI inthe database. These probabilities are a range that can be applied toestimating the likelihood any site, blog, posting or comment will joinan existing conversation.

Assign topic-specific URI- and Site-level outbound linking probabilitiesto each topic-specific URI in the database. These probabilities are arange that can be applied to estimating the likelihood anytopic-specific site, blog, posting or comment will join an existingtopic-specific conversation.

Step H

“Followers” are identified from the sample population. They are siteswith the highest average URI-level probability to create multipleoutbound links.

“Topic Followers” are identified in the sample population. They aresites with the highest average topic-specific URI probability to createmultiple outbound links.

Step 4: Velocity

The Inbounds and Outbounds links data store is examined for linkscreated by new participants during a user-defined timeframe(day/week/two weeks/month). The total number of new participants at alllevels of the hierarchy during the timeframe is subtracted from thetotal new participants in the previous period equal to the user-definedtimeframe. If there are more new participants in the most recenttimeframe, the product will be a negative number, which must beconverted into a positive number in order to divide it by the total ofthe previous timeframe to produce a percentage value between zero [0]and one [1]. If there are fewer participants in the most recenttimeframe, the product will be a positive number, which must beconverted into a negative number in order to divide it by the total ofthe previous timeframe to produce a negative percentage value betweenzero [0] and negative-one [−1].

VI Run Time Analysis to Support Dynamic Analysis of Conversations

One aspect of conversations in social media is that conversations canrapidly propagate and be amplified. In many applications it is desirableto support the capability of an end-user to monitor and engage highlydynamic conversations. As an illustrative example, a marketing personmay want to know what is happening every day to influence a conversationabout a particular product. As another example, in the case of a productdefect, a company executive may want to understand how influence isdynamically changing. It is therefore useful to support a capability toprovide a run time view of influencers for a specific conversation beingqueried. Additionally, in some applications it is desirable toautomatically generate a view of influencers for an end-user on ascheduled basis, such as generating a daily view of influencers for aconversation.

As people publish new social media and trackback, tag, or vote on socialmedia, the network of content grows. In one implementation, theconversation index is updated in a fashion that reflects those changesat query time. That is, the conversation index is updated as socialmedia is published within the conversation index. As previouslydescribed, in one embodiment the calibration process performs researchcrawls for a conversation network. Scheduled crawls (e.g., daily crawls)may be performed to update the conversation index and recalibrations maybe performed to update the content and links in the conversation index.Business rules may be employed to direct spiders to examine both new andexisting social media content which are part of the conversationnetwork. In any case, by updating the content and links in theconversation index for a particular conversation network, a list ofinfluencers can be generated at query time. As will be described belowin more detail, user interfaces may be provide to display a list ofinfluencers at query time and/or according to a schedule.

Note that a conventional search engine cannot be used to generate a listof influencers at query time. As previously described, a conventionalsearch engine does not generate a conversation index from whichinfluencers can be determined. Additionally, a conventional searchengine essentially rely on static snapshots of content that freezesmetadata around each document.

VII Illustrative User Interface and Dashboard Tools

As previously described, one application of the present invention is togenerate a map which is a visual representation of a networkedconversation. In one embodiment of the system, relationship coordinatesand social weight are used to display a map of two degrees of the socialnetwork surrounding a single URI. The map shows only links, not thestrength of relationships, traffic flow and or other characteristics ofthe social relationships between sites as illustrated in FIG. 10. Userscan generate a map by typing a URL in an address bar of a compatible Webbrowser or via a user bookmark to generate a map for a site open in thebrowser. The map's Java-based interface allowed users to mouse over anynode in the map to see its name of the site displayed in the “Site:”field just above the zoom in (“+”) and zoom out (“−”) buttons in theupper left corner of the map. As the user mouses over the nodes in themap, they can active links between sites. Double-clicking any node inthe map opens a new browser window and displays the Web site. Thisprovides a simple way to browse the neighborhood around any site. Themaps open with the site chosen in the center, with the sites it wasdirectly connected to arrayed around that site in a circle.Second-degree connections—the sites connected to the first-degree sitesbut not to the URI mapped—are around the first-degree sites to whichthey are linked. Maps of popular sites could be quite dense and theFengshuinate box can be checked to see the map of how all the sites areinterconnected by rearranging the map to show the most central sites inthe network. Unchecking Fengshuinate froze the map in its newarrangement. Clicking once on any node in the map would reorient the maparound that node. The user could also browse all the sites in the map byclicking the “Jump to:” menu, which displayed a list of all the sites inthe map—selecting a site in the Jump to: menu oriented the map aroundthat site. Changing the map would may make it expand outside theavailable window. Clicking the “Fit” button to resize the mapautomatically. The “Recenter” button place the map back in the middle ofthe window, with the current target URI.

As previously described, one application of the present inventionincludes generating a dashboard user interface. In one implementation,strong ties in the map are highlighted, as illustrated in FIG. 11. Inthis exemplar the map displays the strongest pairwise connections in thesocial network as heavier lines than others displaying connectionsbetween sites. This allows users to see at a glance where the strongestperson-to-person connections within a social network are located. Thedashboard also illustrates how additional analytical results may belayered into the display. In this case the top influencers 1105, topmedia sites, top blog sites, top amplifiers, top new participants(described as “new hits”), top sites where conversations are crossingover with other topics, and top sites where there is no crossover withother topics, to allow the user to browse quickly to find individualsites of interest. This map is also navigable, allowing users to clickon a node to reorient the map around so that they can explore how thenode relates to other traffic, particularly with strong connections. Thedrop-down menus listing top influencers, amplifiers, etc., allow theuser to open a new map oriented on the site they select. Additionally,the lists provide graphical arrows to indicate whether the site listedin rising or falling in the category.

FIG. 12 illustrates a Marketing Dashboard. In this exemplar, the userhas a configurable interface for reviewing a large library of searches,which can be browsed by topic, search string and time period.Additionally, the top sites in various categories (e.g. “TopInfluencers”) are available through a drop-down menu and summary datafor each search is displayed, including the number of sites overall withmatches to the search terms, the number of occurrences of those searchterms, the aggregate tone of the conversation and other data. Otherfeatures include a summary of influencers by type, rank, tone, andreach. Graphical summaries of influencer types and new participants maybe provided. The marketing dashboard has many potential uses, such as inpublic relations.

The design of this dashboard is intended to help marketers reduce thecomplexity of conversational information. Unlike other systems thattrack the appearance of search terms on sites and in blogs, thedashboard provides filters that allow users reduce the population ofparticipants to those with the greatest influence, ability to increasethe velocity of information and other factors. Additionally, the tone ofarticles can be displayed, which permits positive and negative documentsto be identified.

FIG. 13 illustrates a detail navigation. This dashboard includesgraphical information about the sites increasing or decreasing influencein each drop-down list-indicated by a numerical change in ranking-andadditional data about each site in the list, in this case culled fromthe Alexa database that describes network traffic rankings andBuzzLogic-generated data about conversational tone and number of inboundand outbound links. Lists of articles with search term matches aredisplayed on clicking of the site name in each list; these articlelistings, when clicked, opens a browser and displays the content of thearticle. A map similar to those explained above are available through aclickable icon in the drop-down list. As can be seen in FIG. 13, in oneimplementation a list of influencers, such as the 20 top influencers, isprovided. For each influencer the interface permits recent articlelistings to be displayed. Other aspects of influence are displayed. Ascan be understood from FIGS. 12 and 13, the dashboard provides apowerful new tool. Once a conversation topic is defined by a user, theuser can receive a visual display of influencers, summaries of importantaspects of the conversation (such as tone), and quickly access articlesposted by the influencers.

FIG. 14 illustrate a screenshot showing an “influencer view feature.” Alist of influencers is displayed, which is ranked and assigned apercentage score. Filters are provided to filter by media type. A listof all engagements made the post is provided. In this example, the listof influencers corresponds to a list of posts. The list of influencerspermits access to summaries of the corresponding posts, thumbnailimages, date of publication, and number of link relationships both inand out of the post. FIG. 15 illustrates how in one embodiment a socialmap is generated displaying neighbors about a center post. FIG. 16illustrates a screenshot displaying how an engagement with a publisherof an influential post may be recorded.

It will be understood an embodiments of the present invention mayinclude implementing the conversation identification module and socialanalysis modules in a computer readable medium. An embodiment of thepresent invention therefore relates to a computer storage product with acomputer-readable medium having computer code thereon for performingvarious computer-implemented operations. The media and computer code maybe those specially designed and constructed for the purposes of thepresent invention, or they may be of the kind well known and availableto those having skill in the computer software arts. Examples ofcomputer-readable media include, but are not limited to: magnetic mediasuch as hard disks, floppy disks, and magnetic tape; optical media suchas CD-ROMs, DVDs and holographic devices; magneto-optical media; andhardware devices that are specially configured to store and executeprogram code, such as application-specific integrated circuits(“ASICs”), programmable logic devices (“PLDs”) and ROM and RAM devices.Examples of computer code include machine code, such as produced by acompiler, and files containing higher-level code that are executed by acomputer using an interpreter. For example, an embodiment of theinvention may be implemented using Java, C++, or other object-orientedprogramming language and development tools. Another embodiment of theinvention may be implemented in hardwired circuitry in place of, or incombination with, machine-executable software instructions.

The foregoing description, for purposes of explanation, used specificnomenclature to provide a thorough understanding of the invention.However, it will be apparent to one skilled in the art that specificdetails are not required in order to practice the invention. Thus, theforegoing descriptions of specific embodiments of the invention arepresented for purposes of illustration and description. They are notintended to be exhaustive or to limit the invention to the precise formsdisclosed; obviously, many modifications and variations are possible inview of the above teachings. The embodiments were chosen and describedin order to best explain the principles of the invention and itspractical applications, they thereby enable others skilled in the art tobest utilize the invention and various embodiments with variousmodifications as are suited to the particular use contemplated. It isintended that the following claims and their equivalents define thescope of the invention.

What is claimed is:
 1. A method of targeting online advertising, comprising: maintaining a database of historical online content; applying search engine queries to the database to produce search results, wherein the search engine queries correspond to a topic; optimizing the search results for relevance to the topic by applying multiple queries to the database to produce a search result set; determining influence measures for influencers on the topic; ranking relevance to the topic and the influence measures; preparing a list of ranked influencers on the topic; targeting online advertising to an influential publication at a point of engagement for the topic. 